Anna’s Status Report for 03/25

What did you personally accomplish this week on the project? Give files or photos that demonstrate your progress. Prove to the reader that you put sufficient effort into the project over the course of the week (12+ hours).  

This week, for capstone, we spent part of our class time doing the ethics discussion. During the discussion, a few things had been noted by other teams that we hadn’t considered. Namely, a group pointed out that different accents or dialects may affect the pitch of singing. While we had thought about accommodating people with different vocal ranges by using relative pitch, we had not considered it. I am unsure if fully researching this and figuring out a solution is in scope of our project, but it is certainly something we should keep in mind during design.

Next, the web application is now templated for the integration of the pitch detection algorithm. Once the recording begins, sound information is collected every 100ms and calls the ondataavailable method. In this method, both a full version of the recording, and a 10 byte slice of the end of the recording is sent, via POST request, to the Django backend. There is a point in the backend that was set up for Kelly to perform pitch detection. Both audio files had been extracted from the POST request. After this, a json file is sent back to the page JavaScript via Xttp object. After adding in pitch detection, this json will contain the pitch. This value gets updated on the screen.

Additionally, I implemented the updating of the graph to display notes and lyrics. They update via chunk and correspond to the time in an audio file. Each chunk contains pitch data over some period of time, as well as corresponding lyrics.

The chart being used is an stepped line graph made with graph.is. I turned off interactivity and grid features to make it more visually appealing. The values being iterated at the moment are dummy values, but it is a demonstration of how we want the final version to look. We might want to add vertical and horizontal axis labels, as well as a second dataset displaying the user’s performance. This second dataset can be added once the pitch detection is integrated.

Is your progress on schedule or behind? If you are behind, what actions will be taken to catch up to the project schedule?  

I am on schedule. There are insertion points for integration of the pitch detection, as well as basic implementations of all features, which will have to be adjusted as we keep working through combining both aspects of the project.

What deliverables do you hope to complete in the next week?

Next week, I am hoping to have some pitch detection in the web application. At this point, I’d like to combine the graph and recorder, which will also allow us to add the user pitch graphics as well. At that point, the next step will be to create datasets like the dummy one being used now for all chosen songs.